This invention relates to root-mean-square to direct current (RMS-to-DC) signal conversion. More particularly, this invention relates to the filtering of outputs from RMS-to-DC converters that use a sigma-delta modulator.
The RMS value of a signal waveform is the xe2x80x9ceffectivexe2x80x9d value of that waveform. In other words, it is the equivalent DC value that has the same effect as a varying waveform. For example, a sinusoidal voltage waveform having a peak value of 163 volts has an RMS value of about 115 volts. This means that a DC voltage of 115 volts delivers to a resistive load the same power as a sinusoidal voltage having a peak value of 163 volts. RMS measurements allow all types of voltage (and current) waveforms to be compared to one another. Mathematically, the RMS value of a signal V is defined as:
Vrms={overscore ({square root over (V2)})}xe2x80x83xe2x80x83(1)
which means that the RMS value of V is the square root of the mean (average) squared value of V (the overbar in equation (1) represents the average value), and is thus referred to as the xe2x80x9croot-mean-squarexe2x80x9d of V. For sinusoids, the RMS value is 0.707 of the peak value.
Many RMS-to-DC signal conversions involve sampling (i.e., measuring) an analog input signal a specified number of times per second. This is known as the sampling frequency. Outputs of sigma-delta RMS-to-DC converters (and those using other pulse code modulators) typically include quantization noise that can affect the accuracy of the RMS measurement. Quantization is the process of measuring an analog event at a sample time and assigning a numerical value to that measured event. Quantization noise is the difference between the actual analog value at sample time and the quantization value, and generally increases as the analog input signal frequency increases. Quantization noise can also increase when other signal characteristics increase (e.g., amplitude) or change notably (e.g., sine wave to square wave).
A conventional way to deal with this noise is to filter it from the output signal. At low input signal frequencies (relative to the sampling frequency), quantization noise is typically low and therefore requires less filtering to produce accurate RMS results. At high input signal frequencies (relative to the sampling frequency), or upon increases or notable changes in other input signal characteristics, quantization noise is typically not only high, but may be substantial. In these cases, more filtering should be performed to produce accurate RMS results.
The level of filtering, however, affects the response time of the RMS-to-DC conversion process. For example, high levels of filtering, which are preferable when quantization noise is high, result in slow response times. Low levels of filtering, which are preferable when quantization noise is low, result in fast response times. Accordingly, RMS-to-DC conversion involves a trade-off between output signal accuracy (i.e., level of filtering) and response time.
A disadvantage of conventional RMS-to-DC conversion processes is that they are typically limited to a fixed level of noise filtering. Thus, the accuracy versus response time trade-off is probably not optimized for more than one input signal frequency. The likely result is that in some cases response time may be unnecessarily slow because unnecessary filtering is being performed, and in other cases RMS values may be unnecessarily inaccurate because insufficient filtering is being performed.
In view of the foregoing, it would be desirable to provide adaptive filtering of sigma-delta based RMS-to-DC converter outputs in which filter response characteristics are dynamically adjusted in accordance with pre-established accuracy versus response time trade-offs.
It would also be desirable to provide adaptive filtering of sigma-delta based RMS-to-DC converter outputs in which filter response characteristics are dynamically adjusted in response to input signal changes.
It would further be desirable to provide adaptive filtering of sigma-delta based RMS-to-DC converter outputs in which filter response characteristics are dynamically adjusted in response to input signal frequency changes.
It would still further be desirable to provide adaptive filtering of sigma-delta based RMS-to-DC converter outputs such that accurate RMS measurements of very high frequency input signals can be made without adverse noise affects and undue response time increases.
It would yet further be desirable to provide both digital and analog adaptive filtering of sigma-delta based RMS-to-DC converter outputs.
It is an object of this invention to provide adaptive filtering of sigma-delta based RMS-to-DC converter outputs in which filter response characteristics are dynamically adjusted in accordance with pre-established accuracy versus response time trade-offs.
It is also an object of this invention to provide adaptive filtering of sigma-delta based RMS-to-DC converter outputs in which filter response characteristics are dynamically adjusted in response to input signal changes.
It is a further object of this invention to provide adaptive filtering of sigma-delta based RMS-to-DC converter outputs in which filter response characteristics are dynamically adjusted in response to input signal frequency changes.
It is still a further object of this invention to provide adaptive filtering of sigma-delta based RMS-to-DC converter outputs such that accurate RMS measurements of very high frequency input signals can be made without adverse noise affects and undue response time increases.
It is yet a further object of this invention to provide both digital and analog adaptive filtering of sigma-delta based RMS-to-DC converter outputs.
In accordance with this invention, an adaptive filter circuit is provided that filters an electrical signal representing an RMS-to-DC signal conversion of an analog input signal. The representative signal can be, for example, the output signal of an RMS-to-DC converter or the output signal of an analog-to-digital converter (ADC) fed by an RMS-to-DC converter. The filter circuit selects a level of filtering in accordance with criteria that is responsive to at least one signal derived from the analog input signal. The derived signal can be, for example, the output signal of an RMS-to-DC converter, the output signal of an ADC fed by an RMS-to-DC converter, or a signal containing frequency information pertaining to the input signal, such as, for example, the output signal of a frequency counter having the analog input signal as an input. The adaptive filter circuit filters the representative signal in accordance with the selected level of filtering. Embodiments of the present invention include both analog and digital adaptive filtering. Advantageously, the adaptive filter circuit of the present invention dynamically adjusts its response characteristics (e.g., level of filtering and resulting response time) in response to the analog input signal""s characteristics.